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Completed

NCT Number: NCT06116344

Improving Prostate Lesion Classification and Development of a PI-RADS 3 Classifier

The investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier.

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Key information

Conditions

Prostate Cancer Genital Diseases Genital Diseases, Male Genital Neoplasms, Male Male Urogenital Diseases Neoplasms Neoplasms by Site Prostatic Diseases Prostatic Neoplasms Urogenital Diseases Urogenital Neoplasms

Age range

18 year–90 year

Sex eligibility

Male

Study type

Observational

Primary location

Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany

Nuremberg, Germany

About this study

Prostate cancer is the most common carcinoma in male patients in Western industrialized countries. Multiparametric prostate MRI (mpMRI) can select patients who may be potential candidates for biopsy. In this study, the investigators present a comprehensive methodology that evaluates a multitude of AI algorithms and assesses their performance on a large and high-quality dataset, aiming to generate an efficient model and develop a PI-RADS 3 classifier. By combining the power of machine learning with the information provided by mpMRI, histopathological results as well as expert image interpretation, the investigators attempt to improve the diagnostic accuracy, which in the future my lead to more informed clinical decisions and reduce unnecessary biopsies.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Only patients with a clinical indication for mp prostate MRI will be included in this prospective study.
  • No allergies to GBCA

Exclusion criteria

  • Contraindications for MRI

Treatment and study plan

Primary outcomes

  1. Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images

    Time frame: through study completion, an average of 3 years

    Regions of interest for quantitative signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Image analysis will be performed on a PACS workstation. Signal intensity will be measured and normalized, therefore no units needed.

  2. Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images

    Time frame: through study completion, an average of 3 years

    Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s

  3. Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images

    Time frame: through study completion, an average of 3 years

    Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s

  4. Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images

    Time frame: through study completion, an average of 3 years

    Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized. Image analysis will be performed on a PACS workstation. The original Time inteisity curves are transformed in relative enhancement curves. Thus, they are normalized with respect to first point in time and represent the percentage increase compared to the time before contrast arrival, no units needed.

Sponsors and collaborators

Lead sponsor

Paracelsus Medical University

Other

Registry information

Official study title

Improving Prostate Lesion Classification and Diagnostic Accuracy Using Machine Learning: A Comprehensive Evaluation and Development of a PI-RADS 3 Classifier

Important dates

Study start
2018
Primary completion
2020
Study completion
2023
First posted
Nov 3, 2023
Registry last updated
Nov 3, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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